

import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
		import textwrap

		from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
		import torch

		from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
		lowercase			:				List[Any]   =     False


lowercase			:				List[Any]   =     logging.get_logger(__name__)
lowercase			:				Dict   =     "ybelkada/fonts"






def        snake_case__		(    ):
							if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
														raise ImportError(
														    F'You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '
														    '''Pix2StructImageProcessor. Please upgrade torch.'''		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							requires_backends(lowerCamelCase_     ,				['''torch''']		)
							_check_torch_version()

							A      :					List[Any]      		=					image_tensor.unsqueeze(0		)
							A      :					Optional[int]      		=					torch.nn.functional.unfold(lowerCamelCase_     ,				(patch_height, patch_width)     ,				stride=(patch_height, patch_width)		)
							A      :					Union[str, Any]      		=					patches.reshape(image_tensor.size(0		)     ,				image_tensor.size(1		)     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				-1		)
							A      :					Optional[int]      		=					patches.permute(0     ,				4     ,				2     ,				3     ,				1		).reshape(
							    image_tensor.size(2		) // patch_height     ,				image_tensor.size(3		) // patch_width     ,				image_tensor.size(1		) * patch_height * patch_width     ,				)
							return patches.unsqueeze(0		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_ = 36     ,				lowerCamelCase_ = "black"     ,				lowerCamelCase_ = "white"     ,				lowerCamelCase_ = 5     ,				lowerCamelCase_ = 5     ,				lowerCamelCase_ = 5     ,				lowerCamelCase_ = 5     ,				lowerCamelCase_ = None     ,				lowerCamelCase_ = None     ,				):
							requires_backends(lowerCamelCase_     ,				'''vision'''		)
							# Add new lines so that each line is no more than 80 characters.

							A      :					List[Any]      		=					textwrap.TextWrapper(width=80		)
							A      :					str      		=					wrapper.wrap(text=lowerCamelCase_		)
							A      :					Tuple      		=					'''\n'''.join(lowerCamelCase_		)

							if font_bytes is not None and font_path is None:
														A      :					Any      		=					io.BytesIO(lowerCamelCase_		)
							elif font_path is not None:
														A      :					Tuple      		=					font_path
							else:
														A      :					List[str]      		=					hf_hub_download(lowerCamelCase_     ,				'''Arial.TTF'''		)
							A      :					Optional[Any]      		=					ImageFont.truetype(lowerCamelCase_     ,				encoding='''UTF-8'''     ,				size=lowerCamelCase_		)

							# Use a temporary canvas to determine the width and height in pixels when
							# rendering the text.
							A      :					List[str]      		=					ImageDraw.Draw(Image.new('''RGB'''     ,				(1, 1)     ,				lowerCamelCase_		)		)
							A		,		A		,		A		,		A      :					Any      		=					temp_draw.textbbox((0, 0)     ,				lowerCamelCase_     ,				lowerCamelCase_		)

							# Create the actual image with a bit of padding around the text.
							A      :					Union[str, Any]      		=					text_width + left_padding + right_padding
							A      :					List[Any]      		=					text_height + top_padding + bottom_padding
							A      :					List[Any]      		=					Image.new('''RGB'''     ,				(image_width, image_height)     ,				lowerCamelCase_		)
							A      :					int      		=					ImageDraw.Draw(lowerCamelCase_		)
							draw.text(xy=(left_padding, top_padding)     ,				text=lowerCamelCase_     ,				fill=lowerCamelCase_     ,				font=lowerCamelCase_		)
							return image






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				**lowerCamelCase_		):
							requires_backends(lowerCamelCase_     ,				'''vision'''		)

							# Convert to PIL image if necessary
							A      :					str      		=					to_pil_image(lowerCamelCase_		)

							A      :					Dict      		=					render_text(lowerCamelCase_     ,				**lowerCamelCase_		)
							A      :					Tuple      		=					max(header_image.width     ,				image.width		)

							A      :					Optional[Any]      		=					int(image.height * (new_width / image.width)		)
							A      :					Dict      		=					int(header_image.height * (new_width / header_image.width)		)

							A      :					List[str]      		=					Image.new('''RGB'''     ,				(new_width, new_height + new_header_height)     ,				'''white'''		)
							new_image.paste(header_image.resize((new_width, new_header_height)		)     ,				(0, 0)		)
							new_image.paste(image.resize((new_width, new_height)		)     ,				(0, new_header_height)		)

							# Convert back to the original framework if necessary
							A      :					Tuple      		=					to_numpy_array(lowerCamelCase_		)

							if infer_channel_dimension_format(lowerCamelCase_		) == ChannelDimension.LAST:
														A      :					Optional[int]      		=					to_channel_dimension_format(lowerCamelCase_     ,				ChannelDimension.LAST		)

							return new_image







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Union[str, Any]								=	['''flattened_patches''']




							def __init__(					self    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 20_48    ,						__UpperCAmelCase = False    ,						**__UpperCAmelCase    ,						)		->       None:
														super().__init__(**__UpperCAmelCase						)
														A      :					Optional[Any]      		=					patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
														A      :					List[Any]      		=					do_normalize
														A      :					int      		=					do_convert_rgb
														A      :					Dict      		=					max_patches
														A      :					List[str]      		=					is_vqa




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       np.ndarray:
														requires_backends(self.extract_flattened_patches    ,						'''torch'''						)
														_check_torch_version()

														# convert to torch
														A      :					str      		=					to_channel_dimension_format(__UpperCAmelCase    ,						ChannelDimension.FIRST						)
														A      :					int      		=					torch.from_numpy(__UpperCAmelCase						)

														A		,		A      :					Tuple      		=					patch_size['''height'''], patch_size['''width''']
														A		,		A      :					Optional[int]      		=					get_image_size(__UpperCAmelCase						)

														# maximize scale s.t.
														A      :					List[Any]      		=					math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)						)
														A      :					Optional[int]      		=					max(min(math.floor(scale * image_height / patch_height						)    ,						__UpperCAmelCase						)    ,						1						)
														A      :					List[Any]      		=					max(min(math.floor(scale * image_width / patch_width						)    ,						__UpperCAmelCase						)    ,						1						)
														A      :					Dict      		=					max(num_feasible_rows * patch_height    ,						1						)
														A      :					List[Any]      		=					max(num_feasible_cols * patch_width    ,						1						)

														A      :					List[str]      		=					torch.nn.functional.interpolate(
														    image.unsqueeze(0						)    ,						size=(resized_height, resized_width)    ,						mode='''bilinear'''    ,						align_corners=__UpperCAmelCase    ,						antialias=__UpperCAmelCase    ,						).squeeze(0						)

														# [1, rows, columns, patch_height * patch_width * image_channels]
														A      :					List[str]      		=					torch_extract_patches(__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)

														A      :					Tuple      		=					patches.shape
														A      :					Optional[Any]      		=					patches_shape[1]
														A      :					List[Any]      		=					patches_shape[2]
														A      :					Dict      		=					patches_shape[3]

														# [rows * columns, patch_height * patch_width * image_channels]
														A      :					Optional[Any]      		=					patches.reshape([rows * columns, depth]						)

														# [rows * columns, 1]
														A      :					Union[str, Any]      		=					torch.arange(__UpperCAmelCase						).reshape([rows, 1]						).repeat(1    ,						__UpperCAmelCase						).reshape([rows * columns, 1]						)
														A      :					str      		=					torch.arange(__UpperCAmelCase						).reshape([1, columns]						).repeat(__UpperCAmelCase    ,						1						).reshape([rows * columns, 1]						)

														# Offset by 1 so the ids do not contain zeros, which represent padding.
														row_ids += 1
														col_ids += 1

														# Prepare additional patch features.
														# [rows * columns, 1]
														A      :					str      		=					row_ids.to(torch.floataa						)
														A      :					List[str]      		=					col_ids.to(torch.floataa						)

														# [rows * columns, 2 + patch_height * patch_width * image_channels]
														A      :					str      		=					torch.cat([row_ids, col_ids, patches]    ,						-1						)

														# [max_patches, 2 + patch_height * patch_width * image_channels]
														A      :					Tuple      		=					torch.nn.functional.pad(__UpperCAmelCase    ,						[0, 0, 0, max_patches - (rows * columns)]						).float()

														A      :					str      		=					to_numpy_array(__UpperCAmelCase						)

														return result




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase						)		->       np.ndarray:
														if image.dtype == np.uinta:
																					A      :					str      		=					image.astype(np.floataa						)

														# take mean across the whole `image`
														A      :					str      		=					np.mean(__UpperCAmelCase						)
														A      :					List[Any]      		=					np.std(__UpperCAmelCase						)
														A      :					Optional[Any]      		=					max(__UpperCAmelCase    ,						1.0 / math.sqrt(np.prod(image.shape						)						)						)

														return normalize(__UpperCAmelCase    ,						mean=__UpperCAmelCase    ,						std=__UpperCAmelCase    ,						**__UpperCAmelCase						)






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = ChannelDimension.FIRST    ,						**__UpperCAmelCase    ,						)		->       ImageInput:
														A      :					Union[str, Any]      		=					do_normalize if do_normalize is not None else self.do_normalize
														A      :					Union[str, Any]      		=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
														A      :					Tuple      		=					patch_size if patch_size is not None else self.patch_size
														A      :					Optional[Any]      		=					max_patches if max_patches is not None else self.max_patches
														A      :					List[Any]      		=					self.is_vqa

														if kwargs.get('''data_format'''    ,						__UpperCAmelCase						) is not None:
																					raise ValueError('''data_format is not an accepted input as the outputs are '''						)

														A      :					int      		=					make_list_of_images(__UpperCAmelCase						)

														if not valid_images(__UpperCAmelCase						):
																					raise ValueError(
																					    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																					    '''torch.Tensor, tf.Tensor or jax.ndarray.'''						)

														# PIL RGBA images are converted to RGB
														if do_convert_rgb:
																					A      :					Union[str, Any]      		=					[convert_to_rgb(__UpperCAmelCase						) for image in images]

														# All transformations expect numpy arrays.
														A      :					Dict      		=					[to_numpy_array(__UpperCAmelCase						) for image in images]

														if is_vqa:
																					if header_text is None:
																												raise ValueError('''A header text must be provided for VQA models.'''						)
																					A      :					Any      		=					kwargs.pop('''font_bytes'''    ,						__UpperCAmelCase						)
																					A      :					Dict      		=					kwargs.pop('''font_path'''    ,						__UpperCAmelCase						)

																					if isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																												A      :					Optional[Any]      		=					[header_text] * len(__UpperCAmelCase						)

																					A      :					Optional[Any]      		=					[
																					    render_header(__UpperCAmelCase    ,						header_text[i]    ,						font_bytes=__UpperCAmelCase    ,						font_path=__UpperCAmelCase						)
																					    for i, image in enumerate(__UpperCAmelCase						)
																					]

														if do_normalize:
																					A      :					Any      		=					[self.normalize(image=__UpperCAmelCase						) for image in images]

														# convert to torch tensor and permute
														A      :					int      		=					[
														    self.extract_flattened_patches(image=__UpperCAmelCase    ,						max_patches=__UpperCAmelCase    ,						patch_size=__UpperCAmelCase						)
														    for image in images
														]

														# create attention mask in numpy
														A      :					Union[str, Any]      		=					[(image.sum(axis=-1						) != 0).astype(np.floataa						) for image in images]

														A      :					int      		=					BatchFeature(
														    data={'''flattened_patches''': images, '''attention_mask''': attention_masks}    ,						tensor_type=__UpperCAmelCase						)

														return encoded_outputs









import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


lowercase			:				List[str]   =     logging.get_logger(__name__)






def        snake_case__		(    lowerCamelCase_=None     ,				lowerCamelCase_=None		):
							return field(default_factory=lambda: default     ,				metadata=lowerCamelCase_		)







@dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		List[str]								=	list_field(
							    default=[]		,       metadata={
							        '''help''': (
							            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
							            ''' of all available models'''
							        )
							    }		,       )

							UpperCAmelCase_						:		List[int]								=	list_field(
							    default=[8]		,       metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}				)

							UpperCAmelCase_						:		List[int]								=	list_field(
							    default=[8, 32, 1_28, 5_12]		,       metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}		,       )

							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Use FP16 to accelerate inference.'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Benchmark training of model'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Verbose memory tracing'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
							    }		,       )
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Trace memory line by line'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Save result to a CSV file'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Save all print statements in a log file'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to print environment information'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
							            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
							            ''' for debugging / testing and on TPU.'''
							        )
							    }		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'inference_time_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving time results to csv.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'inference_memory_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'train_time_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'train_memory_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'env_info_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving environment information.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'log_{round(time()				)}.csv'		,       metadata={'''help''': '''Log filename used if print statements are saved in log.'''}		,       )
							UpperCAmelCase_						:		int								=	field(default=3		,       metadata={'''help''': '''Times an experiment will be run.'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
							            ''' model weights.'''
							        )
							    }		,       )




							def       snake_case			(					self						)		->       Union[str, Any]:
														warnings.warn(
														    f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
														    ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
														    ''' to benchmark Transformer models.'''    ,						__UpperCAmelCase    ,						)




							def       snake_case			(					self						)		->       Union[str, Any]:
														return json.dumps(dataclasses.asdict(self						)    ,						indent=2						)




							@property
							def       snake_case			(					self						)		->       List[str]:
														if len(self.models						) <= 0:
																					raise ValueError(
																					    '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
																					    ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''						)
														return self.models






							@property
							def       snake_case			(					self						)		->       int:
														if not self.multi_process:
																					return False
														elif self.is_tpu:
																					logger.info('''Multiprocessing is currently not possible on TPU.'''						)
																					return False
														else:
																					return True






